I Built SuperBrain: A Self-Hosted AI Second Brain That Remembers What You Save

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend.

What I Built

SuperBrain is a self-hosted AI-powered second brain for everything you save online.

I built it around a simple problem: we constantly save things we find useful — Instagram posts, YouTube videos, Reddit posts, articles, recipes, tutorials, and ideas — but most of that information eventually disappears into platform-specific "Saved" folders.

You save it today and forget about it tomorrow.

SuperBrain turns those scattered saves into a searchable, organized personal knowledge base.

From Android, you can share an Instagram reel, YouTube video, Reddit post, or webpage directly to SuperBrain. The backend processes the content and uses AI to understand and organize it.

It can generate:

• Meaningful titles
• Summaries
• Categories
• Semantic tags
• Transcriptions
• Audio/music information
• Searchable content

You can then search your saved knowledge, organize it into collections such as Travel, Gym, Recipes, Design, or Watch Later, and receive reminders for things you actually want to revisit.

I built SuperBrain around a real problem faced by someone close to me: constantly saving useful content without having a practical way to find or use it later.

Instead of building another generic chatbot, I wanted to build something that could become a personal memory layer around the information they already consume.

Demo

Live Project: https://superbrain.sid0x.me/

The core workflow is simple:

Find something → Share it → SuperBrain understands it → Find it later.

Saving content

The main workflow starts from something people already do: finding something interesting and hitting Share.

Instead of saving it into another platform-specific folder, the content can be sent directly to SuperBrain.

Once received, SuperBrain processes the content in the background.

AI-powered understanding

SuperBrain doesn't simply store the URL.

It extracts and processes the available content and turns it into structured knowledge.

This means that months later, I don't have to remember which account posted something or which video contained the information.

I can search for the idea itself.

Search Your Own Knowledge

The real value of a second brain isn't saving information.

It's being able to retrieve it when you actually need it.

SuperBrain provides search across the saved knowledge base so content can be discovered without relying on remembering the original URL.

I can also organize saved content into collections.

This turns a growing collection of saved content into something closer to a personal knowledge base.

Smart Reminders

Another problem with saving content is that saving something doesn't mean you'll ever return to it.

SuperBrain includes reminders so useful content can resurface instead of becoming another forgotten bookmark.

How I Built It

SuperBrain is built as a self-hosted Android application backed by a Python API server.

Architecture

                    Android App
               React Native + Expo
                        │
                  Share any URL
                        │
                        ▼
                 FastAPI Backend
                        │
              ┌─────────┴─────────┐
              │                   │
       Content Extraction      AI Router
              │                   │
              │          ┌────────┼────────┐
              │          │        │        │
              │        Groq     Gemini  OpenRouter
              │                             │
              │                           Ollama
              │
              ├── Web pages
              ├── Instagram
              ├── YouTube
              └── Reddit
                        │
                        ▼
                    SQLite
                        │
                        ▼
              Search / Collections
                        │
                        ▼
                 Smart Reminders

The mobile application is built with React Native, Expo and TypeScript.

The backend uses Python, FastAPI and Uvicorn, with SQLite for persistent storage.

AI Model Router

One of the more interesting engineering pieces in SuperBrain is the custom AI model router.

Instead of hard-coding the application to a single AI provider, SuperBrain supports multiple providers and fallback paths.

Text:
Groq → Gemini → OpenRouter → Ollama

Vision:
Gemini → Groq → OpenRouter → Ollama

Transcription:
Groq Whisper → Local Whisper

The idea is simple:

The application should not completely break because one model provider is unavailable.

If a provider fails or becomes unavailable, the router can fall back to another available provider.

This also makes the system easier to experiment with because the underlying model can be changed without rebuilding the entire application.

Multimodal Processing

SuperBrain isn't limited to plain text.

Depending on the content, the system can work with:

• Text
• Images
• Video
• Audio
• Speech
• Web pages
• Instagram content
• YouTube content
• Reddit content

Video and audio processing allow SuperBrain to extract useful information from content that would otherwise be difficult to search.

Self-Hosting

One of the goals of SuperBrain is to make the backend deployable on infrastructure that the user controls.

It can be run on a personal computer, Linux server, Raspberry Pi, or cloud VM.

The project also supports Docker-based deployment.

npx -y superbrain-server@latest

This makes it possible to run the system without requiring a permanently hosted proprietary backend.

Why Does Open Innovation Matter?

A personal second brain contains some of the most sensitive information a person can have:

• Personal notes
• Saved content
• Ideas
• Documents
• Interests
• Search history
• Knowledge accumulated over time

That makes control over the AI stack particularly important.

SuperBrain is designed so that the AI layer doesn't have to belong to one closed provider.

The system can use different models and includes Ollama and local Whisper fallbacks, allowing parts of the AI workload to run locally.

Privacy

Personal knowledge can remain on infrastructure controlled by the user.

Model freedom

The underlying model can be changed without redesigning the entire application.

Self-hosting

The backend can run on hardware or infrastructure that the user controls.

Resilience

If one AI provider becomes unavailable or reaches a rate limit, the application can fall back to another provider.

Experimentation

Open models make it possible to experiment with different models and inference setups without locking the entire product to a single vendor.

For me, that's the biggest value of open innovation here:

The AI is a component of the system, not the system itself.

The application controls the ingestion, processing, storage, retrieval, routing, fallback logic, notifications, and user experience.

What I Learned

Building SuperBrain taught me that the difficult part of an AI application isn't simply calling an LLM.

The interesting engineering problems appear around it:

• Reliable content extraction
• Multimodal processing
• Model fallbacks
• Rate limits
• Provider failures
• Local inference
• Persistent storage
• Mobile-to-server communication
• Background processing
• Search
• Notifications
• Self-hosted deployment

The result is something much closer to a real product than a simple AI wrapper.

Code

The complete source code is available on GitHub:

github.com/sidinsearch/superbrain

The repository contains the application, backend, deployment configuration, and supporting infrastructure.

Prize Categories

I'm entering only partner categories whose technology is genuinely used by SuperBrain.

• Open-source AI / open innovation
• [Add applicable partner categories here]

Try SuperBrain

Live: https://superbrain.sid0x.me/

GitHub: https://github.com/sidinsearch/superbrain

If you've ever saved hundreds of things online and then completely forgotten where they were, that's exactly the problem SuperBrain is trying to solve.